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ENHANCED FINDABILITY AND REUSABILITY OF ENGINEERING DATA BY CONTEXTUAL METADATA

Published online by Cambridge University Press:  19 June 2023

Osman Altun*
Affiliation:
Leibniz University Hannover, Institute of Product Development
Pooya Oladazimi
Affiliation:
Leibniz Information Centre of Science and Technology University Library
Max Leo Wawer
Affiliation:
Leibniz University Hannover, Institute of Product Development
Selina Raumel
Affiliation:
Leibniz University Hannover, Institute of Micro Production Technology
Marc Wurz
Affiliation:
Leibniz University Hannover, Institute of Micro Production Technology
Khemais Barienti
Affiliation:
Leibniz University Hannover, Institut für Werkstoffkunde (Materials Science)
Florian Nürnberger
Affiliation:
Leibniz University Hannover, Institut für Werkstoffkunde (Materials Science)
Roland Lachmayer
Affiliation:
Leibniz University Hannover, Institute of Product Development
Iryna Mozgova
Affiliation:
Paderborn University, Data management in mechanical engineering
Oliver Koepler
Affiliation:
Leibniz Information Centre of Science and Technology University Library
Sören Auer
Affiliation:
Leibniz Information Centre of Science and Technology University Library
*
Altun, Osman, Leibniz University Hannover, Germany, altun@ipeg.uni-hannover.de

Abstract

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Complex research problems are increasingly addressed by interdisciplinary, collaborate research projects generating large amounts of heterogeneous amounts of data. The overarching processing, analysis and availability of data are critical success factors for these research efforts. Data repositories enable long term availability of such data for the scientific community. The findability and therefore reusability strongly builds on comprehensive annotations of datasets stored in repositories. Often generic metadata schema are used to annotate data. In this publication we describe the implementation of discipline specific metadata into a data repository to provide more contextual information about data. To avoid extra workload for researchers to provide such metadata a workflow with standardised data templates for automated metadata extraction during the ingest process has been developed. The enriched metadata are in the following used in the development of two repository plugins for data comparison and data visualisation. The added values of discipline-specific annotations and derived search features to support matching and reusable data is then demonstrated by use cases of two Collaborative Research Centres (CRC 1368 and CRC 1153).

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Altun, O., Scheveleva, T., Castro, A., Oladazimi, P., Koepler, O., Mozgova, I., Lachmayer, R., and Auer, S. (2021), “Integration eines digitalen Maschinenparks in ein Forschungsdatenmanagementsystem”, Proceedings of the 32nd Symposium Design for X (DFX2021). https://doi.org/10.35199/dfx2021.23CrossRefGoogle Scholar
Amorim, R.C., Castro, J.A., Rocha da Silva, J. and Ribeiro, C. (2017), “A comparison of research datamanagement platforms: architecture, flexible metadata and interoperability”, Univ. Access Inf Soc, Vol. 16, pp. 851862. https://doi.org/10.1007/s10209-016-0475-yCrossRefGoogle Scholar
Beer, A., Brunet, M., Srivastava, V. and Vidal, M.E. (2022), “Leibniz Data Manager – A Research Data Management System”, In: Groth, P., Rula, A., et al. , The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, Vol. 13384, Springer, Cham, pp. 7377. https://doi.org/10.1007/978-3-031-11609-4_14CrossRefGoogle Scholar
Büttner, S., Hobohm, H.-C. and Müller, L. (2011), Handbuch Forschungsdatenmanagement, Bock + Herchen Verlag, Bad Honnef.Google Scholar
Devaraju, A., Mokrane, M., Cepinskas, L., Huber, R., Herterich, P., de Vries, J., Akerman, V., L'Hours, H., Davidson, J. and Diepenbroek, M. (2021), “From Conceptualization to Implementation: FAIR Assessment of Research Data Objects”, Data Science Journal, Vol. 20, No. 4, pp. 114. https:// doi.org/10.5334/dsj-2021-004CrossRefGoogle Scholar
Effertz, E. (2010), “The Funder's Perspective: Data Management in Coordinated Programmes of the German Research Foundation (DFG)”, In Bareth, G., Curdt, C. (ed.), Proceedings of the Data Management Workshop, Geographisches Institut der Universität zu Köln, Cologne. http://dx.doi.org/10.5880/TR32DB.KGA90.7Google Scholar
Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D. and Roberts, J.C. (2011), “Visual comparison for information visualization”, Information Visualization, Vol. 10, No. 4 pp. 289309. https://doi.org/10.1177/1473871611416549CrossRefGoogle Scholar
Hartl, N., Wössner, E. and Sure-Vetter, Y. (2021), “Nationale Forschungsdateninfrastruktur (NFDI)”, Informatik Spektrum, Vol. 44, No. 5, pp. 370373, Springer. https://doi.org/10.1007/s00287-021-01392-6CrossRefGoogle Scholar
Hordych, I., Barienti, K., Herbst, S., Maier, H.J. and Nürnberger, F. (2021), “Cold Roll Bonding of Tin-Coated Steel Sheets with Subsequent Heat Treatment”, Metals, Vol. 11, p. 917. http://dx.doi.org/10.3390/met11060917CrossRefGoogle Scholar
Kapogiannis, G. and Sherratt, F.(2018), “Impact of integrated collaborative technologies to form a collaborative culture in construction projects”, Built Environment Project and Asset Management, Vol. 8 No. 1, pp. 2438. https://doi.org/10.1108/BEPAM-07-2017-0043CrossRefGoogle Scholar
Koppe, R., Gerchow, P., Macario, A., Haas, A., Schäfer-Neth, C. and Pfeiffenberger, H. (2015), “O2A: A generic framework for enabling the flow of sensor observations to archives and publications,” OCEANS 2015, Genova, pp. 16. https://doi.org/10.1109/OCEANS-Genova.2015.7271657Google Scholar
Lamprecht, A.-L. et al. (2017), “Towards FAIR Principles for Research Software”, Data Science Journal, Vol. 3, No. 1, pp. 3759. http://dx.doi.org/10.3233/DS-190026CrossRefGoogle Scholar
Maali, F. and Erickson, J. (2020), Data Catalog Vocabulary (DCAT) - Version 2. W3C Recommendation [online], Available at: https://www.w3.org/TR/vocab-dcat-2/(02.11.2022).Google Scholar
Mozgova, I., Koepler, O., Kraft, A., Lachmayer, R. and Auer, S. (2020). “Research data management system for a large collaborative project”, Proceedings of NordDesign 2020, Lyngby, Denmark. https://doi.org/10.35199/NORDDESIGN2020.48CrossRefGoogle Scholar
Mozgova, I., Altun, O., Sheveleva, T., Castro, A., Oladazimi, P., Lachmayer, R. and Auer, S. (2022), “Knowledge Annotation within Research Data Management System for Oxygen-Free Production Technologies”, Proceeding of Design Society 2022, Vol. 2, pp. 525532. http://dx.doi.org/10.1017/pds.2022.54CrossRefGoogle Scholar
Raumel, S., Barienti, K., Dencker, F., Nürnberger, F. and Wurz, M.C. (2021), “Einfluss von Silan dotierten Umgebungsatmosphären auf die tribologischen Eigenschaften von Titan”, Tribologie und Schmierungstechnik, Vol. 68, pp. 513. http://dx.doi.org/10.24053/tus-2021-0002Google Scholar
Sandfeld, S., Dahmen, T., Fischer, F.O.R., Eberl, C., Klein, S., Selzer, M., Nestler, B., Möller, J., Mücklich, F., Engstler, M., Diebels, S., Tschuncky, R., Prakash, A., Steinberger, D., Kübel, C., Herrmann, H.-G. and Schubotz, R. (2018), Strategiepapier Digitale Transformation in der Materialwissenschaft und Werkstofftechnik. Deutsche Gesellschaft für Materialkunde. Available at: https://edocs.tib.eu/files/e01fn18/1028913559.pdf. (01.11.2022).Google Scholar
Schultes, E. and Wittenburg, P. (2019), “FAIR Principles and Digital Objects: Accelerating Convergence on a Data Infrastructure”, In: Manolopoulos, Y., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, Vol. 1003, Springer, Cham. https://doi.org/10.1007/978-3-030-23584-0_1Google Scholar
Sheveleva, T., Wawer, M.L., Oladazimi, P., Koepler, O., Nürnberger, F., Lachmayer, R., Auer, S., Mozgova, I. (2022), “Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering”, IFAC-PapersOnLine, Vol. 55, No. 10, pp. 20302035. https://doi.org/10.1016/j.ifacol.2022.10.006CrossRefGoogle Scholar
Wilkinson, M.D., Dumontier, M., Aalbersberg, I., Appleton, G., Axton, M., Baak, A., Blomberg, N. et al. (2016), “The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, Vol. 3. https://doi.org/10.1038/sdata.2016.18CrossRefGoogle ScholarPubMed